Recent Trends in IoT–Based Smart Healthcare Applying ML and DL

Internet of Things (IoT) is an interconnected network of different sensors, communication channels, cloud data storage, and application software. Applying machine learning algorithms in IoT-based smart healthcare systems can achieve a significant growth in prediction and detection of diseases. This review aims to provide recent research trend in various directions of smart healthcare application. A systematic methodology based on extensive search has been followed to identify most relevant works for further detailed review and investigation. A total number of (n = 836) papers are screened published during 2014–2019 from most popular databases such as IEEE Xplore, Science Direct, Springer Link and Semantic Scholar. After two stage filtering, only 24 most relevant papers are identified and categorized in 4 different categories. In first category is disease identification and diagnosis where 54.16% (n = 13/24) paper belongs, 29.16% (n = 7/24) papers belongs to second category, patient monitoring using IoT. Rests 12.50% (n = 3/24) and 4.18% (n = 1/24) papers belongs to category 3 and 4, drug discovery, personalized treatment and smart health record, respectively.